Overview

Brought to you by YData

Dataset statistics

Number of variables19
Number of observations11065619
Missing cells0
Missing cells (%)0.0%
Duplicate rows107
Duplicate rows (%)< 0.1%
Total size in memory1.6 GiB
Average record size in memory152.0 B

Variable types

Categorical1
Numeric16
DateTime2

Alerts

Dataset has 107 (< 0.1%) duplicate rowsDuplicates
ESP32_temp is highly overall correlated with MACHigh correlation
MAC is highly overall correlated with ESP32_temp and 3 other fieldsHigh correlation
WORKSTATION_CPU is highly overall correlated with WORKSTATION_CPU_POWER and 8 other fieldsHigh correlation
WORKSTATION_CPU_POWER is highly overall correlated with MAC and 10 other fieldsHigh correlation
WORKSTATION_CPU_TEMP is highly overall correlated with WORKSTATION_CPU and 9 other fieldsHigh correlation
WORKSTATION_GPU_POWER is highly overall correlated with WORKSTATION_CPU and 8 other fieldsHigh correlation
WORKSTATION_GPU_TEMP is highly overall correlated with WORKSTATION_CPU and 7 other fieldsHigh correlation
WORKSTATION_RAM is highly overall correlated with MAC and 9 other fieldsHigh correlation
WORKSTATION_RAM_POWER is highly overall correlated with WORKSTATION_CPU and 8 other fieldsHigh correlation
corriente is highly overall correlated with WORKSTATION_CPU and 3 other fieldsHigh correlation
energia is highly overall correlated with WORKSTATION_CPU_POWER and 3 other fieldsHigh correlation
fp is highly overall correlated with WORKSTATION_CPU_POWER and 2 other fieldsHigh correlation
potencia is highly overall correlated with WORKSTATION_CPU and 8 other fieldsHigh correlation
voltaje is highly overall correlated with MAC and 8 other fieldsHigh correlation
MAC is highly imbalanced (80.9%)Imbalance
ESP32_temp has 3061149 (27.7%) zerosZeros
WORKSTATION_CPU has 6764177 (61.1%) zerosZeros
WORKSTATION_CPU_POWER has 7758484 (70.1%) zerosZeros
WORKSTATION_CPU_TEMP has 7758486 (70.1%) zerosZeros
WORKSTATION_GPU has 10793423 (97.5%) zerosZeros
WORKSTATION_GPU_POWER has 7758486 (70.1%) zerosZeros
WORKSTATION_GPU_TEMP has 8065750 (72.9%) zerosZeros
WORKSTATION_RAM has 6675954 (60.3%) zerosZeros
WORKSTATION_RAM_POWER has 7758486 (70.1%) zerosZeros

Reproduction

Analysis started2025-10-19 08:58:15.981662
Analysis finished2025-10-19 09:06:45.537145
Duration8 minutes and 29.56 seconds
Software versionydata-profiling vv4.17.0
Download configurationconfig.json

Variables

MAC
Categorical

High correlation  Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size84.4 MiB
3C:61:05:12:96:30
10740067 
AC:67:B2:3D:62:80
 
325552

Length

Max length17
Median length17
Mean length17
Min length17

Characters and Unicode

Total characters188115523
Distinct characters14
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3C:61:05:12:96:30
2nd row3C:61:05:12:96:30
3rd row3C:61:05:12:96:30
4th row3C:61:05:12:96:30
5th row3C:61:05:12:96:30

Common Values

ValueCountFrequency (%)
3C:61:05:12:96:3010740067
97.1%
AC:67:B2:3D:62:80325552
 
2.9%

Length

2025-10-19T17:06:45.771656image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2025-10-19T17:06:45.804531image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ValueCountFrequency (%)
3c:61:05:12:96:3010740067
97.1%
ac:67:b2:3d:62:80325552
 
2.9%

Most occurring characters

ValueCountFrequency (%)
:55328095
29.4%
622131238
 
11.8%
321805686
 
11.6%
021805686
 
11.6%
121480134
 
11.4%
211391171
 
6.1%
C11065619
 
5.9%
510740067
 
5.7%
910740067
 
5.7%
A325552
 
0.2%
Other values (4)1302208
 
0.7%

Most occurring categories

ValueCountFrequency (%)
(unknown)188115523
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
:55328095
29.4%
622131238
 
11.8%
321805686
 
11.6%
021805686
 
11.6%
121480134
 
11.4%
211391171
 
6.1%
C11065619
 
5.9%
510740067
 
5.7%
910740067
 
5.7%
A325552
 
0.2%
Other values (4)1302208
 
0.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown)188115523
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
:55328095
29.4%
622131238
 
11.8%
321805686
 
11.6%
021805686
 
11.6%
121480134
 
11.4%
211391171
 
6.1%
C11065619
 
5.9%
510740067
 
5.7%
910740067
 
5.7%
A325552
 
0.2%
Other values (4)1302208
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown)188115523
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
:55328095
29.4%
622131238
 
11.8%
321805686
 
11.6%
021805686
 
11.6%
121480134
 
11.4%
211391171
 
6.1%
C11065619
 
5.9%
510740067
 
5.7%
910740067
 
5.7%
A325552
 
0.2%
Other values (4)1302208
 
0.7%

weekday
Real number (ℝ)

Distinct7
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1034053
Minimum1
Maximum7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.4 MiB
2025-10-19T17:06:45.829399image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median4
Q36
95-th percentile7
Maximum7
Range6
Interquartile range (IQR)4

Descriptive statistics

Standard deviation1.9884778
Coefficient of variation (CV)0.4845921
Kurtosis-1.2117191
Mean4.1034053
Median Absolute Deviation (MAD)2
Skewness-0.1130189
Sum45406720
Variance3.9540441
MonotonicityNot monotonic
2025-10-19T17:06:45.859722image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
41793610
16.2%
51790363
16.2%
61659657
15.0%
71605471
14.5%
11540765
13.9%
21483798
13.4%
31191955
10.8%
ValueCountFrequency (%)
11540765
13.9%
21483798
13.4%
31191955
10.8%
41793610
16.2%
51790363
16.2%
61659657
15.0%
71605471
14.5%
ValueCountFrequency (%)
71605471
14.5%
61659657
15.0%
51790363
16.2%
41793610
16.2%
31191955
10.8%
21483798
13.4%
11540765
13.9%
Distinct9347835
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Memory size84.4 MiB
Minimum2021-05-05 22:05:27
Maximum2021-12-04 08:18:12
Invalid dates0
Invalid dates (%)0.0%
2025-10-19T17:06:45.902224image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:06:45.952809image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
Distinct9352784
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Memory size84.4 MiB
Minimum2021-05-05 22:05:27
Maximum2021-12-04 08:18:12
Invalid dates0
Invalid dates (%)0.0%
2025-10-19T17:06:46.002180image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:06:46.053923image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

voltaje
Real number (ℝ)

High correlation 

Distinct43
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.02132
Minimum116.1
Maximum120.6
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.4 MiB
2025-10-19T17:06:46.104455image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum116.1
5-th percentile119.6
Q1120
median120.1
Q3120.1
95-th percentile120.5
Maximum120.6
Range4.5
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.30542714
Coefficient of variation (CV)0.0025447739
Kurtosis16.340703
Mean120.02132
Median Absolute Deviation (MAD)0.1
Skewness-3.4240325
Sum1.3281102 × 109
Variance0.093285736
MonotonicityNot monotonic
2025-10-19T17:06:46.149169image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1203497786
31.6%
120.13420896
30.9%
120.21675175
15.1%
119.9976188
 
8.8%
120.5579419
 
5.2%
119.6353741
 
3.2%
119.896220
 
0.9%
118.386795
 
0.8%
118.985433
 
0.8%
120.466245
 
0.6%
Other values (33)227721
 
2.1%
ValueCountFrequency (%)
116.13
 
< 0.1%
116.23
 
< 0.1%
116.51
 
< 0.1%
116.71
 
< 0.1%
116.84
 
< 0.1%
116.95
< 0.1%
11710
< 0.1%
117.14
 
< 0.1%
117.27
< 0.1%
117.38
< 0.1%
ValueCountFrequency (%)
120.6788
 
< 0.1%
120.5579419
 
5.2%
120.466245
 
0.6%
120.315
 
< 0.1%
120.21675175
15.1%
120.13420896
30.9%
1203497786
31.6%
119.9976188
 
8.8%
119.896220
 
0.9%
119.712005
 
0.1%

corriente
Real number (ℝ)

High correlation 

Distinct1092
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82034394
Minimum0.02
Maximum2.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.4 MiB
2025-10-19T17:06:46.196641image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0.02
5-th percentile0.13
Q10.68
median0.92
Q30.94
95-th percentile1.057
Maximum2.1
Range2.08
Interquartile range (IQR)0.26

Descriptive statistics

Standard deviation0.23061584
Coefficient of variation (CV)0.28112091
Kurtosis2.1688513
Mean0.82034394
Median Absolute Deviation (MAD)0.11
Skewness-1.421553
Sum9077613.5
Variance0.053183665
MonotonicityNot monotonic
2025-10-19T17:06:46.246187image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.921561932
14.1%
0.931446578
 
13.1%
0.621101668
 
10.0%
0.94813172
 
7.3%
0.13640347
 
5.8%
0.61639726
 
5.8%
0.69411616
 
3.7%
1.03292740
 
2.6%
1.04275381
 
2.5%
1.02257373
 
2.3%
Other values (1082)3625086
32.8%
ValueCountFrequency (%)
0.025995
 
0.1%
0.0985
 
< 0.1%
0.12
 
< 0.1%
0.114
 
< 0.1%
0.1210
 
< 0.1%
0.13640347
5.8%
0.163
 
< 0.1%
0.192
 
< 0.1%
0.211
 
< 0.1%
0.222
 
< 0.1%
ValueCountFrequency (%)
2.14
< 0.1%
2.093
< 0.1%
2.081
 
< 0.1%
1.981
 
< 0.1%
1.9761
 
< 0.1%
1.9571
 
< 0.1%
1.9441
 
< 0.1%
1.943
< 0.1%
1.935
< 0.1%
1.9282
 
< 0.1%

potencia
Real number (ℝ)

High correlation 

Distinct1368
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.191899
Minimum0
Maximum245.6
Zeros6074
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size84.4 MiB
2025-10-19T17:06:46.291798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile4.9
Q176.6
median95
Q398.8
95-th percentile113.9
Maximum245.6
Range245.6
Interquartile range (IQR)22.2

Descriptive statistics

Standard deviation25.869717
Coefficient of variation (CV)0.29669862
Kurtosis3.4431944
Mean87.191899
Median Absolute Deviation (MAD)13.3
Skewness-1.6896999
Sum9.6483234 × 108
Variance669.24224
MonotonicityNot monotonic
2025-10-19T17:06:46.337534image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.9402450
 
3.6%
94.9355265
 
3.2%
94.8351782
 
3.2%
95285556
 
2.6%
94.7259319
 
2.3%
4.8237737
 
2.1%
66.1218663
 
2.0%
95.1216869
 
2.0%
66.2187526
 
1.7%
66179935
 
1.6%
Other values (1358)8370517
75.6%
ValueCountFrequency (%)
06074
0.1%
0.45
 
< 0.1%
0.81
 
< 0.1%
1.11
 
< 0.1%
1.21
 
< 0.1%
2.21
 
< 0.1%
2.52
 
< 0.1%
2.91
 
< 0.1%
3.81
 
< 0.1%
4.12
 
< 0.1%
ValueCountFrequency (%)
245.61
< 0.1%
245.11
< 0.1%
2452
< 0.1%
244.61
< 0.1%
244.51
< 0.1%
244.21
< 0.1%
243.31
< 0.1%
226.61
< 0.1%
223.31
< 0.1%
221.11
< 0.1%

frecuencia
Real number (ℝ)

Distinct9
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.96468
Minimum59.3
Maximum60.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size84.4 MiB
2025-10-19T17:06:46.372100image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum59.3
5-th percentile59.9
Q159.9
median60
Q360
95-th percentile60
Maximum60.3
Range1
Interquartile range (IQR)0.1

Descriptive statistics

Standard deviation0.049704949
Coefficient of variation (CV)0.00082890377
Kurtosis-0.7624344
Mean59.96468
Median Absolute Deviation (MAD)0
Skewness-0.82633497
Sum6.635463 × 108
Variance0.002470582
MonotonicityNot monotonic
2025-10-19T17:06:46.402306image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
607250525
65.5%
59.93712301
33.5%
59.899556
 
0.9%
60.13170
 
< 0.1%
59.746
 
< 0.1%
60.218
 
< 0.1%
60.31
 
< 0.1%
59.31
 
< 0.1%
59.41
 
< 0.1%
ValueCountFrequency (%)
59.31
 
< 0.1%
59.41
 
< 0.1%
59.746
 
< 0.1%
59.899556
 
0.9%
59.93712301
33.5%
607250525
65.5%
60.13170
 
< 0.1%
60.218
 
< 0.1%
60.31
 
< 0.1%
ValueCountFrequency (%)
60.31
 
< 0.1%
60.218
 
< 0.1%
60.13170
 
< 0.1%
607250525
65.5%
59.93712301
33.5%
59.899556
 
0.9%
59.746
 
< 0.1%
59.41
 
< 0.1%
59.31
 
< 0.1%

energia
Real number (ℝ)

High correlation 

Distinct45882
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean122.39697
Minimum0
Maximum442.626
Zeros82
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size84.4 MiB
2025-10-19T17:06:46.441885image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6.46
Q138.31
median119.99
Q3173.52
95-th percentile276.77
Maximum442.626
Range442.626
Interquartile range (IQR)135.21

Descriptive statistics

Standard deviation98.100056
Coefficient of variation (CV)0.80149087
Kurtosis1.4894105
Mean122.39697
Median Absolute Deviation (MAD)72.76
Skewness1.1365584
Sum1.3543983 × 109
Variance9623.6209
MonotonicityNot monotonic
2025-10-19T17:06:46.485123image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
137.17730
 
0.1%
137.437723
 
0.1%
136.997720
 
0.1%
137.297719
 
0.1%
137.247719
 
0.1%
136.937718
 
0.1%
137.077717
 
0.1%
136.67716
 
0.1%
136.857715
 
0.1%
136.497714
 
0.1%
Other values (45872)10988428
99.3%
ValueCountFrequency (%)
082
 
< 0.1%
0.02570
< 0.1%
0.03687
< 0.1%
0.04678
< 0.1%
0.05760
< 0.1%
0.06704
< 0.1%
0.07638
< 0.1%
0.08651
< 0.1%
0.09759
< 0.1%
0.1686
< 0.1%
ValueCountFrequency (%)
442.62613
< 0.1%
442.62531
< 0.1%
442.62425
< 0.1%
442.62331
< 0.1%
442.62226
< 0.1%
442.62129
< 0.1%
442.6231
< 0.1%
442.61925
< 0.1%
442.61830
< 0.1%
442.61725
< 0.1%

fp
Real number (ℝ)

High correlation 

Distinct57
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.86025335
Minimum0
Maximum1
Zeros6074
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size84.4 MiB
2025-10-19T17:06:46.529773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.32
Q10.86
median0.89
Q30.9
95-th percentile0.97
Maximum1
Range1
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.13945405
Coefficient of variation (CV)0.16210811
Kurtosis10.851852
Mean0.86025335
Median Absolute Deviation (MAD)0.03
Skewness-3.4260506
Sum9519235.8
Variance0.019447431
MonotonicityNot monotonic
2025-10-19T17:06:46.577069image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.863253719
29.4%
0.92399702
21.7%
0.891296058
 
11.7%
0.94678932
 
6.1%
0.32609354
 
5.5%
0.97538312
 
4.9%
0.85391609
 
3.5%
0.95361836
 
3.3%
0.87328359
 
3.0%
0.91299376
 
2.7%
Other values (47)908362
 
8.2%
ValueCountFrequency (%)
06074
0.1%
0.034
 
< 0.1%
0.041
 
< 0.1%
0.071
 
< 0.1%
0.091
 
< 0.1%
0.11
 
< 0.1%
0.171
 
< 0.1%
0.192
 
< 0.1%
0.211
 
< 0.1%
0.271
 
< 0.1%
ValueCountFrequency (%)
1903
 
< 0.1%
0.99303
 
< 0.1%
0.9835033
 
0.3%
0.97538312
4.9%
0.96156648
 
1.4%
0.95361836
3.3%
0.94678932
6.1%
0.93298199
2.7%
0.92248311
 
2.2%
0.91299376
2.7%

ESP32_temp
Real number (ℝ)

High correlation  Zeros 

Distinct87
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.100912
Minimum0
Maximum53.3333
Zeros3061149
Zeros (%)27.7%
Negative0
Negative (%)0.0%
Memory size84.4 MiB
2025-10-19T17:06:46.620823image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median31.67
Q333.89
95-th percentile53.3333
Maximum53.3333
Range53.3333
Interquartile range (IQR)33.89

Descriptive statistics

Standard deviation15.987543
Coefficient of variation (CV)0.66335841
Kurtosis-0.83834116
Mean24.100912
Median Absolute Deviation (MAD)4.45
Skewness-0.47949705
Sum2.6669151 × 108
Variance255.60153
MonotonicityNot monotonic
2025-10-19T17:06:46.666276image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
03061149
27.7%
33.891957975
17.7%
33.331318189
11.9%
34.44669242
 
6.0%
53.3333660831
 
6.0%
26.67580106
 
5.2%
27.22517443
 
4.7%
32.78413152
 
3.7%
26.11358328
 
3.2%
32.22335751
 
3.0%
Other values (77)1193453
 
10.8%
ValueCountFrequency (%)
03061149
27.7%
20.55561
 
< 0.1%
20.561
 
< 0.1%
21.11111
 
< 0.1%
21.66672
 
< 0.1%
21.6713
 
< 0.1%
22.22774
 
< 0.1%
22.22223
 
< 0.1%
22.7823626
 
0.2%
23.3321378
 
0.2%
ValueCountFrequency (%)
53.3333660831
6.0%
52.777851
 
< 0.1%
52.222274
 
< 0.1%
51.666756
 
< 0.1%
51.111155
 
< 0.1%
50.555664
 
< 0.1%
5061
 
< 0.1%
49.444442
 
< 0.1%
48.888961
 
< 0.1%
48.333351
 
< 0.1%

WORKSTATION_CPU
Real number (ℝ)

High correlation  Zeros 

Distinct2696
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9972231
Minimum0
Maximum100
Zeros6764177
Zeros (%)61.1%
Negative0
Negative (%)0.0%
Memory size84.4 MiB
2025-10-19T17:06:46.710870image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34.43
95-th percentile8.85
Maximum100
Range100
Interquartile range (IQR)4.43

Descriptive statistics

Standard deviation3.3134864
Coefficient of variation (CV)1.6590467
Kurtosis8.2015163
Mean1.9972231
Median Absolute Deviation (MAD)0
Skewness2.0132835
Sum22100510
Variance10.979192
MonotonicityNot monotonic
2025-10-19T17:06:46.755009image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06764177
61.1%
0.1187572
 
1.7%
0.2177318
 
1.6%
0.3145380
 
1.3%
4.43129114
 
1.2%
4.3101019
 
0.9%
0.498748
 
0.9%
4.5690528
 
0.8%
4.3688841
 
0.8%
4.4982023
 
0.7%
Other values (2686)3200899
28.9%
ValueCountFrequency (%)
06764177
61.1%
0.1187572
 
1.7%
0.2177318
 
1.6%
0.261
 
< 0.1%
0.3145380
 
1.3%
0.498748
 
0.9%
0.552733
 
0.5%
0.629489
 
0.3%
0.711675
 
0.1%
0.88304
 
0.1%
ValueCountFrequency (%)
1006
< 0.1%
99.85
< 0.1%
99.621
 
< 0.1%
99.091
 
< 0.1%
98.64
< 0.1%
97.531
 
< 0.1%
96.741
 
< 0.1%
96.611
 
< 0.1%
96.222
 
< 0.1%
95.441
 
< 0.1%

WORKSTATION_CPU_POWER
Real number (ℝ)

High correlation  Zeros 

Distinct1225
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.360714
Minimum0
Maximum46.29
Zeros7758484
Zeros (%)70.1%
Negative0
Negative (%)0.0%
Memory size84.4 MiB
2025-10-19T17:06:46.797738image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q336.97
95-th percentile40.43
Maximum46.29
Range46.29
Interquartile range (IQR)36.97

Descriptive statistics

Standard deviation17.427578
Coefficient of variation (CV)1.5340214
Kurtosis-1.1953699
Mean11.360714
Median Absolute Deviation (MAD)0
Skewness0.88883636
Sum1.2571334 × 108
Variance303.72049
MonotonicityNot monotonic
2025-10-19T17:06:46.840345image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07758484
70.1%
37.6778647
 
0.7%
37.5871798
 
0.6%
37.6849000
 
0.4%
37.3647579
 
0.4%
38.2541225
 
0.4%
37.6640317
 
0.4%
37.6140279
 
0.4%
37.7736555
 
0.3%
37.7236175
 
0.3%
Other values (1215)2865560
 
25.9%
ValueCountFrequency (%)
07758484
70.1%
33.32313
 
< 0.1%
33.33545
 
< 0.1%
33.34358
 
< 0.1%
33.35317
 
< 0.1%
33.36252
 
< 0.1%
33.37211
 
< 0.1%
33.38179
 
< 0.1%
33.39198
 
< 0.1%
33.4198
 
< 0.1%
ValueCountFrequency (%)
46.294
< 0.1%
46.076
< 0.1%
45.974
< 0.1%
45.944
< 0.1%
45.931
 
< 0.1%
45.914
< 0.1%
45.94
< 0.1%
45.883
< 0.1%
45.877
< 0.1%
45.854
< 0.1%

WORKSTATION_CPU_TEMP
Real number (ℝ)

High correlation  Zeros 

Distinct68
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6656744
Minimum0
Maximum165
Zeros7758486
Zeros (%)70.1%
Negative0
Negative (%)0.0%
Memory size84.4 MiB
2025-10-19T17:06:46.883564image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q326
95-th percentile33
Maximum165
Range165
Interquartile range (IQR)26

Descriptive statistics

Standard deviation13.483595
Coefficient of variation (CV)1.5559776
Kurtosis-0.78430312
Mean8.6656744
Median Absolute Deviation (MAD)0
Skewness0.99625473
Sum95891051
Variance181.80734
MonotonicityNot monotonic
2025-10-19T17:06:46.926893image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07758486
70.1%
27751880
 
6.8%
26640354
 
5.8%
28407809
 
3.7%
25268911
 
2.4%
29236227
 
2.1%
30158598
 
1.4%
31119613
 
1.1%
3287037
 
0.8%
3684514
 
0.8%
Other values (58)552190
 
5.0%
ValueCountFrequency (%)
07758486
70.1%
182
 
< 0.1%
2165
 
< 0.1%
22176
 
< 0.1%
2314306
 
0.1%
2461615
 
0.6%
25268911
 
2.4%
26640354
 
5.8%
27751880
 
6.8%
28407809
 
3.7%
ValueCountFrequency (%)
1651
 
< 0.1%
981
 
< 0.1%
881
 
< 0.1%
841
 
< 0.1%
832
 
< 0.1%
827
< 0.1%
812
 
< 0.1%
801
 
< 0.1%
791
 
< 0.1%
7811
< 0.1%

WORKSTATION_GPU
Real number (ℝ)

Zeros 

Distinct52
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.035774151
Minimum0
Maximum63
Zeros10793423
Zeros (%)97.5%
Negative0
Negative (%)0.0%
Memory size84.4 MiB
2025-10-19T17:06:46.971223image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum63
Range63
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.31441047
Coefficient of variation (CV)8.7887611
Kurtosis928.91307
Mean0.035774151
Median Absolute Deviation (MAD)0
Skewness19.8574
Sum395863.13
Variance0.098853944
MonotonicityNot monotonic
2025-10-19T17:06:47.014650image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
010793423
97.5%
1179570
 
1.6%
233668
 
0.3%
318130
 
0.2%
0.0117904
 
0.2%
47229
 
0.1%
54581
 
< 0.1%
0.022359
 
< 0.1%
62194
 
< 0.1%
0.031987
 
< 0.1%
Other values (42)4574
 
< 0.1%
ValueCountFrequency (%)
010793423
97.5%
0.0117904
 
0.2%
0.022359
 
< 0.1%
0.031987
 
< 0.1%
0.04666
 
< 0.1%
0.05285
 
< 0.1%
0.06142
 
< 0.1%
0.07149
 
< 0.1%
0.0881
 
< 0.1%
0.0948
 
< 0.1%
ValueCountFrequency (%)
631
 
< 0.1%
571
 
< 0.1%
402
 
< 0.1%
341
 
< 0.1%
304
< 0.1%
293
 
< 0.1%
287
< 0.1%
267
< 0.1%
258
< 0.1%
243
 
< 0.1%

WORKSTATION_GPU_POWER
Real number (ℝ)

High correlation  Zeros 

Distinct18
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.115553
Minimum0
Maximum43
Zeros7758486
Zeros (%)70.1%
Negative0
Negative (%)0.0%
Memory size84.4 MiB
2025-10-19T17:06:47.050203image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q333
95-th percentile35
Maximum43
Range43
Interquartile range (IQR)33

Descriptive statistics

Standard deviation15.517828
Coefficient of variation (CV)1.5340563
Kurtosis-1.1976624
Mean10.115553
Median Absolute Deviation (MAD)0
Skewness0.88851294
Sum1.1193485 × 108
Variance240.80299
MonotonicityNot monotonic
2025-10-19T17:06:47.080817image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
07758486
70.1%
341210054
 
10.9%
35836395
 
7.6%
33523682
 
4.7%
36293044
 
2.6%
32185113
 
1.7%
3181382
 
0.7%
3075011
 
0.7%
2960348
 
0.5%
2727058
 
0.2%
Other values (8)15046
 
0.1%
ValueCountFrequency (%)
07758486
70.1%
2727058
 
0.2%
285810
 
0.1%
2960348
 
0.5%
3075011
 
0.7%
3181382
 
0.7%
32185113
 
1.7%
33523682
 
4.7%
341210054
 
10.9%
35836395
 
7.6%
ValueCountFrequency (%)
4314
 
< 0.1%
42226
 
< 0.1%
41698
 
< 0.1%
40478
 
< 0.1%
39227
 
< 0.1%
38351
 
< 0.1%
377242
 
0.1%
36293044
 
2.6%
35836395
7.6%
341210054
10.9%

WORKSTATION_GPU_TEMP
Real number (ℝ)

High correlation  Zeros 

Distinct337
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8942826
Minimum0
Maximum51.04
Zeros8065750
Zeros (%)72.9%
Negative0
Negative (%)0.0%
Memory size84.4 MiB
2025-10-19T17:06:47.117798image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q314.13
95-th percentile14.51
Maximum51.04
Range51.04
Interquartile range (IQR)14.13

Descriptive statistics

Standard deviation6.3977863
Coefficient of variation (CV)1.6428665
Kurtosis-0.66022634
Mean3.8942826
Median Absolute Deviation (MAD)0
Skewness1.0621819
Sum43092648
Variance40.931669
MonotonicityNot monotonic
2025-10-19T17:06:47.163200image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
08065750
72.9%
14.32569430
 
5.1%
14.42486260
 
4.4%
14.22337750
 
3.1%
14.33281392
 
2.5%
14.23241006
 
2.2%
14.13179944
 
1.6%
14.52156379
 
1.4%
14.51134180
 
1.2%
14.43126337
 
1.1%
Other values (327)487191
 
4.4%
ValueCountFrequency (%)
08065750
72.9%
13.081
 
< 0.1%
13.181
 
< 0.1%
13.2731
 
< 0.1%
13.2817
 
< 0.1%
13.351
 
< 0.1%
13.3626
 
< 0.1%
13.37216
 
< 0.1%
13.4537
 
< 0.1%
13.46110
 
< 0.1%
ValueCountFrequency (%)
51.041
 
< 0.1%
50.661
 
< 0.1%
50.571
 
< 0.1%
50.481
 
< 0.1%
50.381
 
< 0.1%
50.372
< 0.1%
50.292
< 0.1%
50.221
 
< 0.1%
50.213
< 0.1%
50.112
< 0.1%

WORKSTATION_RAM
Real number (ℝ)

High correlation  Zeros 

Distinct3560
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.779082
Minimum0
Maximum55.2
Zeros6675954
Zeros (%)60.3%
Negative0
Negative (%)0.0%
Memory size84.4 MiB
2025-10-19T17:06:47.208086image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q337.21
95-th percentile44.11
Maximum55.2
Range55.2
Interquartile range (IQR)37.21

Descriptive statistics

Standard deviation18.691217
Coefficient of variation (CV)1.2647075
Kurtosis-1.5639621
Mean14.779082
Median Absolute Deviation (MAD)0
Skewness0.55196455
Sum1.635397 × 108
Variance349.36159
MonotonicityNot monotonic
2025-10-19T17:06:47.254411image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
06675954
60.3%
42.525973
 
0.2%
42.722949
 
0.2%
47.521099
 
0.2%
44.320924
 
0.2%
47.420756
 
0.2%
38.9520305
 
0.2%
38.9620194
 
0.2%
45.920136
 
0.2%
42.320102
 
0.2%
Other values (3550)4197227
37.9%
ValueCountFrequency (%)
06675954
60.3%
11.371
 
< 0.1%
11.3840
 
< 0.1%
11.3929
 
< 0.1%
11.4161
 
< 0.1%
11.4162
 
< 0.1%
11.4215
 
< 0.1%
11.4356
 
< 0.1%
11.4421
 
< 0.1%
11.4545
 
< 0.1%
ValueCountFrequency (%)
55.24
 
< 0.1%
55.110
< 0.1%
5517
< 0.1%
54.921
< 0.1%
54.84
 
< 0.1%
54.710
< 0.1%
54.68
 
< 0.1%
54.513
< 0.1%
54.312
< 0.1%
54.214
< 0.1%

WORKSTATION_RAM_POWER
Real number (ℝ)

High correlation  Zeros 

Distinct1696
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4286255
Minimum0
Maximum46.41
Zeros7758486
Zeros (%)70.1%
Negative0
Negative (%)0.0%
Memory size84.4 MiB
2025-10-19T17:06:47.298955image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q35.44
95-th percentile10.59
Maximum46.41
Range46.41
Interquartile range (IQR)5.44

Descriptive statistics

Standard deviation4.0146946
Coefficient of variation (CV)1.6530727
Kurtosis0.58053689
Mean2.4286255
Median Absolute Deviation (MAD)0
Skewness1.3643011
Sum26874244
Variance16.117773
MonotonicityNot monotonic
2025-10-19T17:06:47.345576image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
07758486
70.1%
7.935832
 
0.1%
7.945823
 
0.1%
8.075815
 
0.1%
8.145801
 
0.1%
8.125800
 
0.1%
7.965793
 
0.1%
8.095779
 
0.1%
8.155777
 
0.1%
8.255772
 
0.1%
Other values (1686)3254941
29.4%
ValueCountFrequency (%)
07758486
70.1%
2.11
 
< 0.1%
2.131
 
< 0.1%
2.141
 
< 0.1%
2.183
 
< 0.1%
2.211
 
< 0.1%
2.241
 
< 0.1%
2.251
 
< 0.1%
2.273
 
< 0.1%
2.283
 
< 0.1%
ValueCountFrequency (%)
46.411
 
< 0.1%
34.751
 
< 0.1%
27.651
 
< 0.1%
24.871
 
< 0.1%
22.481
 
< 0.1%
22.112
 
< 0.1%
21.945
< 0.1%
21.781
 
< 0.1%
21.631
 
< 0.1%
21.591
 
< 0.1%

Interactions

2025-10-19T17:05:51.080528image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:04.448440image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:15.288259image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:25.771506image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:37.711165image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:49.560296image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:04:00.167436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:04:10.040540image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:04:21.089459image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:04:34.665993image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:04:48.123512image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:04:58.381962image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:05:09.460386image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:05:18.953562image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:05:29.486789image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:05:40.509937image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:05:51.701495image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:05.270441image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:15.841215image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:26.388436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:38.378686image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:50.229204image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:04:00.911807image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:04:10.583467image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:04:22.017526image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:04:35.531433image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:04:48.822293image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:04:59.024592image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:05:10.043976image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:05:19.567948image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:05:30.127672image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:05:41.125864image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:05:52.315148image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:06.000889image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:16.583679image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:26.944881image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:38.976414image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
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2025-10-19T17:05:39.099599image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:05:49.748347image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:06:00.799557image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:14.649044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:25.077429image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:37.058816image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:48.889773image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:03:59.494644image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:04:09.391454image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:04:19.874014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:04:33.873845image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:04:47.416015image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:04:57.720866image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:05:08.807014image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:05:18.283044image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:05:28.880591image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:05:39.843182image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
2025-10-19T17:05:50.459436image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/

Correlations

2025-10-19T17:06:47.402784image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
ESP32_tempMACWORKSTATION_CPUWORKSTATION_CPU_POWERWORKSTATION_CPU_TEMPWORKSTATION_GPUWORKSTATION_GPU_POWERWORKSTATION_GPU_TEMPWORKSTATION_RAMWORKSTATION_RAM_POWERcorrienteenergiafpfrecuenciapotenciavoltajeweekday
ESP32_temp1.0000.6980.2270.1340.1010.1590.1340.0120.2200.109-0.2980.3940.3720.014-0.2260.1520.115
MAC0.6981.0000.0190.6900.2740.0250.3370.1060.8340.2720.2450.2740.2300.0280.1241.0000.171
WORKSTATION_CPU0.2270.0191.0000.8970.9020.2640.8870.8330.8860.8920.5720.4410.4360.0060.625-0.606-0.007
WORKSTATION_CPU_POWER0.1340.6900.8971.0000.9820.2770.9830.9380.7260.9660.4760.5400.5090.0020.540-0.549-0.017
WORKSTATION_CPU_TEMP0.1010.2740.9020.9821.0000.2450.9710.9370.7130.9890.4800.5300.5210.0010.545-0.547-0.016
WORKSTATION_GPU0.1590.0250.2640.2770.2451.0000.2590.2410.1900.2140.0920.0900.1360.0050.128-0.133-0.005
WORKSTATION_GPU_POWER0.1340.3370.8870.9830.9710.2591.0000.9330.7240.9600.4910.5450.4960.0010.549-0.5570.009
WORKSTATION_GPU_TEMP0.0120.1060.8330.9380.9370.2410.9331.0000.6980.9020.4530.6070.4570.0080.516-0.458-0.027
WORKSTATION_RAM0.2200.8340.8860.7260.7130.1900.7240.6981.0000.6990.5980.4490.2620.0110.616-0.567-0.031
WORKSTATION_RAM_POWER0.1090.2720.8920.9660.9890.2140.9600.9020.6991.0000.4730.4910.529-0.0020.537-0.557-0.013
corriente-0.2980.2450.5720.4760.4800.0920.4910.4530.5980.4731.000-0.045-0.028-0.0060.962-0.821-0.045
energia0.3940.2740.4410.5400.5300.0900.5450.6070.4490.491-0.0451.0000.4640.0180.0080.008-0.024
fp0.3720.2300.4360.5090.5210.1360.4960.4570.2620.529-0.0280.4641.0000.0080.110-0.1880.025
frecuencia0.0140.0280.0060.0020.0010.0050.0010.0080.011-0.002-0.0060.0180.0081.000-0.0030.121-0.001
potencia-0.2260.1240.6250.5400.5450.1280.5490.5160.6160.5370.9620.0080.110-0.0031.000-0.842-0.043
voltaje0.1521.000-0.606-0.549-0.547-0.133-0.557-0.458-0.567-0.557-0.8210.008-0.1880.121-0.8421.0000.048
weekday0.1150.171-0.007-0.017-0.016-0.0050.009-0.027-0.031-0.013-0.045-0.0240.025-0.001-0.0430.0481.000

Missing values

2025-10-19T17:06:02.961904image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
A simple visualization of nullity by column.
2025-10-19T17:06:13.944314image/svg+xmlMatplotlib v3.10.0, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

MACweekdayfecha_servidorfecha_esp32voltajecorrientepotenciafrecuenciaenergiafpESP32_tempWORKSTATION_CPUWORKSTATION_CPU_POWERWORKSTATION_CPU_TEMPWORKSTATION_GPUWORKSTATION_GPU_POWERWORKSTATION_GPU_TEMPWORKSTATION_RAMWORKSTATION_RAM_POWER
03C:61:05:12:96:3042021-05-06 10:00:002021-05-06 10:00:00120.10.9396.360.01.160.860.00.00.000.000.00.00.0
13C:61:05:12:96:3042021-05-06 10:00:012021-05-06 10:00:01120.10.9396.359.91.160.860.00.00.000.000.00.00.0
23C:61:05:12:96:3042021-05-06 10:00:012021-05-06 10:00:01120.00.9496.659.91.160.860.00.00.000.000.00.00.0
33C:61:05:12:96:3042021-05-06 10:00:022021-05-06 10:00:02120.00.9496.659.91.160.860.00.00.000.000.00.00.0
43C:61:05:12:96:3042021-05-06 10:00:032021-05-06 10:00:03120.00.9496.659.91.160.860.00.00.000.000.00.00.0
53C:61:05:12:96:3042021-05-06 10:00:032021-05-06 10:00:03120.10.9396.359.91.160.860.00.00.000.000.00.00.0
63C:61:05:12:96:3042021-05-06 10:00:032021-05-06 10:00:03120.10.9396.360.01.160.860.00.00.000.000.00.00.0
73C:61:05:12:96:3042021-05-06 10:00:042021-05-06 10:00:04120.10.9396.360.01.160.860.00.00.000.000.00.00.0
83C:61:05:12:96:3042021-05-06 10:00:042021-05-06 10:00:04120.00.9396.860.01.160.860.00.00.000.000.00.00.0
93C:61:05:12:96:3042021-05-06 10:00:052021-05-06 10:00:05120.00.9396.860.01.160.860.00.00.000.000.00.00.0
MACweekdayfecha_servidorfecha_esp32voltajecorrientepotenciafrecuenciaenergiafpESP32_tempWORKSTATION_CPUWORKSTATION_CPU_POWERWORKSTATION_CPU_TEMPWORKSTATION_GPUWORKSTATION_GPU_POWERWORKSTATION_GPU_TEMPWORKSTATION_RAMWORKSTATION_RAM_POWER
110656093C:61:05:12:96:3062021-12-04 08:18:082021-12-04 08:18:08119.61.047111.959.9442.6260.8953.333313.6938.38340.03414.1348.565.36
11065610AC:67:B2:3D:62:8062021-12-04 08:18:082021-12-04 08:18:08118.80.70180.359.924.8500.9653.33335.2134.68250.0300.0024.958.69
110656113C:61:05:12:96:3062021-12-04 08:18:082021-12-04 08:18:09119.61.031110.559.9442.6260.9053.33337.5438.38260.03414.3348.553.89
11065612AC:67:B2:3D:62:8062021-12-04 08:18:092021-12-04 08:18:09118.80.70781.459.924.8500.9753.33335.2634.68250.0300.0024.958.36
110656133C:61:05:12:96:3062021-12-04 08:18:092021-12-04 08:18:10119.61.152125.460.0442.6260.9153.33335.7738.38250.03414.3348.553.63
11065614AC:67:B2:3D:62:8062021-12-04 08:18:102021-12-04 08:18:10118.80.71282.359.924.8500.9753.33335.0834.68260.0300.0024.958.31
110656153C:61:05:12:96:3062021-12-04 08:18:112021-12-04 08:18:11119.61.152125.459.9442.6260.9153.333313.1838.38350.03414.2348.585.58
11065616AC:67:B2:3D:62:8062021-12-04 08:18:112021-12-04 08:18:11118.90.67578.559.924.8500.9853.33335.2134.68260.0300.0024.948.31
110656173C:61:05:12:96:3062021-12-04 08:18:112021-12-04 08:18:12119.61.189130.359.9442.6260.9253.333313.1838.38350.03414.2348.585.58
11065618AC:67:B2:3D:62:8062021-12-04 08:18:122021-12-04 08:18:12118.80.69180.059.924.8500.9753.33334.4334.68250.0300.0024.957.71

Duplicate rows

Most frequently occurring

MACweekdayfecha_servidorfecha_esp32voltajecorrientepotenciafrecuenciaenergiafpESP32_tempWORKSTATION_CPUWORKSTATION_CPU_POWERWORKSTATION_CPU_TEMPWORKSTATION_GPUWORKSTATION_GPU_POWERWORKSTATION_GPU_TEMPWORKSTATION_RAMWORKSTATION_RAM_POWER# duplicates
03C:61:05:12:96:3042021-05-06 10:00:002021-05-06 10:00:00120.10.9396.360.01.160.860.00.00.000.000.00.00.02
13C:61:05:12:96:3042021-05-06 10:00:012021-05-06 10:00:01120.00.9496.659.91.160.860.00.00.000.000.00.00.02
23C:61:05:12:96:3042021-05-06 10:00:012021-05-06 10:00:01120.10.9396.359.91.160.860.00.00.000.000.00.00.02
33C:61:05:12:96:3042021-05-06 10:00:022021-05-06 10:00:02120.00.9496.659.91.160.860.00.00.000.000.00.00.02
43C:61:05:12:96:3042021-05-06 10:00:032021-05-06 10:00:03120.00.9496.659.91.160.860.00.00.000.000.00.00.02
53C:61:05:12:96:3042021-05-06 10:00:032021-05-06 10:00:03120.10.9396.359.91.160.860.00.00.000.000.00.00.02
63C:61:05:12:96:3042021-05-06 10:00:032021-05-06 10:00:03120.10.9396.360.01.160.860.00.00.000.000.00.00.02
73C:61:05:12:96:3042021-05-06 10:00:042021-05-06 10:00:04120.00.9396.860.01.160.860.00.00.000.000.00.00.02
83C:61:05:12:96:3042021-05-06 10:00:042021-05-06 10:00:04120.10.9396.360.01.160.860.00.00.000.000.00.00.02
93C:61:05:12:96:3042021-05-06 10:00:052021-05-06 10:00:05120.00.9396.459.91.160.860.00.00.000.000.00.00.02